TARGET-HF : developing a model for detecting incident heart failure among symptomatic patients in general practice using routine health care data
© The Author(s) 2022. Published by Oxford University Press..
BACKGROUND: Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data.
METHODS: Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor.
RESULTS: Data from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n = 25,524) and validation (n = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P < 0.001).
CONCLUSION: The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:40 |
|---|---|
| Enthalten in: |
Family practice - 40(2023), 1 vom: 09. Feb., Seite 188-194 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
De Clercq, Lukas [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Decision support techniques |
|---|
| Anmerkungen: |
Date Completed 10.02.2023 Date Revised 18.09.2023 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1093/fampra/cmac069 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM342995278 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM342995278 | ||
| 003 | DE-627 | ||
| 005 | 20231226015405.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/fampra/cmac069 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1143.xml |
| 035 | |a (DE-627)NLM342995278 | ||
| 035 | |a (NLM)35778772 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a De Clercq, Lukas |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a TARGET-HF |b developing a model for detecting incident heart failure among symptomatic patients in general practice using routine health care data |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 10.02.2023 | ||
| 500 | |a Date Revised 18.09.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2022. Published by Oxford University Press. | ||
| 520 | |a BACKGROUND: Timely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data | ||
| 520 | |a METHODS: Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor | ||
| 520 | |a RESULTS: Data from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n = 25,524) and validation (n = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P < 0.001) | ||
| 520 | |a CONCLUSION: The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a decision support techniques | |
| 650 | 4 | |a dyspnoea | |
| 650 | 4 | |a early diagnosis | |
| 650 | 4 | |a heart failure | |
| 650 | 4 | |a oedema | |
| 650 | 4 | |a primary health care | |
| 700 | 1 | |a Schut, Martijn C |e verfasserin |4 aut | |
| 700 | 1 | |a Bossuyt, Patrick M M |e verfasserin |4 aut | |
| 700 | 1 | |a van Weert, Henk C P M |e verfasserin |4 aut | |
| 700 | 1 | |a Handoko, M Louis |e verfasserin |4 aut | |
| 700 | 1 | |a Harskamp, Ralf E |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Family practice |d 1993 |g 40(2023), 1 vom: 09. Feb., Seite 188-194 |w (DE-627)NLM013359614 |x 1460-2229 |7 nnns |
| 773 | 1 | 8 | |g volume:40 |g year:2023 |g number:1 |g day:09 |g month:02 |g pages:188-194 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/fampra/cmac069 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 40 |j 2023 |e 1 |b 09 |c 02 |h 188-194 | ||